IFTD: Image Feature Triangle Descriptor for Loop Detection in Driving Scenes

TL;DR

This paper introduces IFTD, a fast and robust triangle descriptor based on keypoints from BEV images, improving place recognition accuracy and efficiency in driving scenes with low computational cost.

Contribution

The paper presents a novel triangle descriptor method for place recognition that leverages BEV keypoints and achieves superior robustness and accuracy over existing methods.

Findings

Outperforms state-of-the-art methods in accuracy and robustness

Operates with low computational overhead

Effective in diverse driving scenarios

Abstract

In this work, we propose a fast and robust Image Feature Triangle Descriptor (IFTD) based on the STD method, aimed at improving the efficiency and accuracy of place recognition in driving scenarios. We extract keypoints from BEV projection image of point cloud and construct these keypoints into triangle descriptors. By matching these feature triangles, we achieved precise place recognition and calculated the 4-DOF pose estimation between two keyframes. Furthermore, we employ image similarity inspection to perform the final place recognition. Experimental results on three public datasets demonstrate that our IFTD can achieve greater robustness and accuracy than state-of-the-art methods with low computational overhead.